Bayesian analysis of spatial data using different variance and neighbourhood structures

• Autor(a) principal: Rampaso, Renato Couto [UNESP]
• Data de Publicação: 2016
• Outros Autores: Pires de Souza, Aparecida Doniseti [UNESP], Flores, Edilson Ferreira [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1080/00949655.2015.1022549; http://hdl.handle.net/11449/164959
• topic: conditional autoregressive models; disease mapping; spatial Bayesian inference

In disease mapping, the overall goal is to study the incidence or mortality risk caused by a specific disease in a number of geographical regions. It is common to assume that the response variable follows a Poisson distribution, whose average rate can be explained by a group of covariates and a random effect. For this random effect, it is considered conditional autoregressive (CAR) models, which carry information about the neighbourhood relationship between the regions. The focus of this paper was to explore and compare some CAR models proposed in the literature. An application with epidemiological data was conducted to model the risk of death due to Crohn's Disease and Ulcerative Colitis in the State of SAo Paulo - Brazil. Finally, a simulation study was done to strengthen the results and assess the performance of the models in the presence of various levels of spatial dependence.